A Model Registry is a centralized system that manages and tracks machine learning models throughout their lifecycle. It serves as a repository where models can be stored, versioned, and organized, allowing data scientists and engineers to efficiently manage different iterations of their models. By providing metadata about each model, such as training data, performance metrics, and deployment status, a Model Registry facilitates collaboration and reproducibility in machine learning projects. This tool is essential for maintaining a clear overview of model experiments, promoting best practices in model management, and ensuring that the most effective models are readily accessible for deployment.